A Chang'E-1 global catalog of lunar impact craters

A New Global Database of Lunar Impact Craters >1–2 km: 1. Crater Locations and Sizes, Comparisons With Published Databases, and Global Analysis

“…Fourth, Wang et al () has >2× more craters D ≳ 100 km than any other researcher (and 30% more D ≥ 200 km than the GRAIL‐based catalog), but they are near or slightly below parity relative to this catalog and the Head et al () work for D ≈ 30–100 km craters. For D ≲ 30 km, the relative fraction again drops with decreasing diameter, though less quickly than the Salamunićcar et al () data.…”

“…Cataloging lunar impacts has a long history, but it is only recently that researchers have been able to produce global catalogs, and even more recently that we have been able to produce global catalogs from a reasonably consistent image base. While the catalogs vary in numbers of craters D > 5 km (27,931 for Head et al, with Povilaitis et al, ; 30,123 for Barlow, ; and 38,171 for Wang et al, ), and some of that variation will be due to inclusion or exclusion of secondary craters, they all have tens of thousands of craters of that diameter and larger. It is likely because of this large number that only automated efforts have so far been used on the Moon to compile larger catalogs to smaller crater diameters.…”

“…The former was stated to be complete for craters ≳8 km. Wang et al () published an automated crater catalog of 106,016 craters made from Chang'E‐1 (嫦娥一号) images and DTM. The paper did not suggest a completeness limit other than ≥10 pixels, corresponding to D > 1.2 km, and it estimated a detection rate of 75.8%.…”

An Iterative Black Top Hat Transform Algorithm for the Volume Estimation of Lunar Impact Craters

“…We merged (bthmask*) as (ibthmask) and selected the mean (ibth*) as a candidate to calculate crater volume. On the merged (ibthmask), the isolated and small islands that were not real crater parts were filtered automatically by using the mapped crater rim polygon [27] as the threshold layer to identify crater areas as (finibthmask). The final ibth was the mean (ibth*) selected by the (finibthmask) to calculate the crater volume according to Equation 1 slope value with increment ∆s, until they reached the maximum (rmax) and (smax).…”

Automatic mapping of lunar landforms using DEM-derived geomorphometric parameters

“…Thus, a series of automated methods has been developed to identify individual terrain features; physical characteristics can be extracted from a DEM using a variety of methods including the combination of morphometric parameters like slope gradient, local convexity, and surface texture (Iwahashi and Pike, 2007), as well as fuzzy logic and unsupervised classification (Burrough et al, 2000;Adediran et al, 2004), supervised classification (Hengl and Rossiter, 2003;Prima et al, 2006), probabilistic clustering algorithms (Stepinski and Collier, 2004;Stepinski and Vilalta, 2005), multivariate descriptive statistics (Dehn et al, 2001), and double ternary diagram classification (Bolongaro-Crevenna et al, 2005). Although these methods have been applied to data from other planets, including Mars (Stepinski and Collier, 2004) and the Moon (Wang et al, 2015), they are too specific in the context of their respective fields of application to be successfully adapted for landscape classification over broad spatial scales. In addition, most of these applications are unable to adequately evaluate the characteristics of lunar landforms even though digital computers and GIS methods have removed many of the obstacles inherent to terrain classification based on surface geometry for areas of any size or suitable spatial resolution.…”